# -*- coding: utf-8 -*-  
import numpy as np  
from matplotlib.font_manager import FontProperties
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.pyplot as plt

x = [1,2,3,4,5,6,7]

ap = [0.2422,0.6798,0.3861,0.3058,0.7079,0.7108,0.8681]
ar = [0.1856,0.5215,0.2796,0.2853,0.6925,0.6949,0.8622]
af = [0.2102,0.5902,0.3243,0.2952,0.7001,0.7028,0.8652]
ia = [0.1856,0.5215,0.2796,0.2853,0.6925,0.6949,0.8622]
plt.figure(figsize=(8,4))
plt.plot(x,ap,'ro-',label="$macro-precision$",color="red",linewidth=2)
plt.plot(x,ar,'gv-',label="$macro-recall$",color="green",linewidth=2)
plt.plot(x,af,'bs-',label="$macro-F$",color="blue",linewidth=2)
plt.plot(x,ia,'ch-',label="$micro-average$",color="black",linewidth=2)
plt.title(u'The classification Effect based on  Balanced corpus',fontsize =20)
plt.xlim(1, 7)
plt.ylim(0.15, 1.05)
group_labels = ['F1+F2', 'F1+F2+F3','F1+F2+F4','F1+F2+F5','F1+F3+F4+F5','F1+F2+F3+F4+F5','F1+F2+F3+F4+F5+F6']
plt.xticks(x, group_labels, rotation=0,fontsize =16)
plt.legend()
plt.grid()
plt.show()